Anuj Pareek
YOU?
Author Swipe
View article: Enhancing Anterior Quadratus Lumborum Block Accuracy with Artificial Intelligence: A Segmentation Approach Evaluated by Dice Score Metrics
Enhancing Anterior Quadratus Lumborum Block Accuracy with Artificial Intelligence: A Segmentation Approach Evaluated by Dice Score Metrics Open
Anterior quadratus lumborum (QL) block is a regional anesthesia technique shown to provide both somatic and visceral pain relief by targeting lower thoracic nerves and the thoracic sympathetic trunk. Despite its clinical benefits, success …
View article: A Simple PTAS for Weighted $k$-means and Sensor Coverage
A Simple PTAS for Weighted $k$-means and Sensor Coverage Open
Clustering is a fundamental technique in data analysis, with the $k$-means being one of the widely studied objectives due to its simplicity and broad applicability. In many practical scenarios, data points come with associated weights that…
View article: A Dataset for Understanding Radiologist-Artificial Intelligence Collaboration
A Dataset for Understanding Radiologist-Artificial Intelligence Collaboration Open
This dataset, Collab-CXR, provides a unique resource to study human-AI collaboration in chest X-ray interpretation. We present experimentally generated data from 227 professional radiologists who assessed 324 historical cases under varying…
View article: Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis
Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis Open
Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models in downstream tasks like classification and segmentation. …
View article: Adapted large language models can outperform medical experts in clinical text summarization
Adapted large language models can outperform medical experts in clinical text summarization Open
View article: Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts
Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts Open
Sifting through vast textual data and summarizing key information from electronic health records (EHR) imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in …
View article: Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization
Adapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization Open
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language pro…
View article: RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models Open
We systematically investigate lightweight strategies to adapt large language models (LLMs) for the task of radiology report summarization (RRS). Specifically, we focus on domain adaptation via pretraining (on natural language, biomedical t…
View article: Self-supervised learning for medical image classification: a systematic review and implementation guidelines
Self-supervised learning for medical image classification: a systematic review and implementation guidelines Open
Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes. However, the prevailing paradigm of training deep learning models requires lar…
View article: The Effect of Counterfactuals on Reading Chest X-rays
The Effect of Counterfactuals on Reading Chest X-rays Open
This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays. We conduct a reader study with two radiologists assessing 240 chest X-ray predictions to rate their confidence that the model's predictio…
View article: Figure S2 from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation
Figure S2 from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation Open
In Figure S2 iron oxide nanoparticle distribution corresponds with TAM distribution on histopathology. (A) Prussian blue stain (blue) of a bone sarcoma shows similar iron distribution and co-localization with (B) CD68 and (C) CD163 macroph…
View article: Figure S2 from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation
Figure S2 from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation Open
In Figure S2 iron oxide nanoparticle distribution corresponds with TAM distribution on histopathology. (A) Prussian blue stain (blue) of a bone sarcoma shows similar iron distribution and co-localization with (B) CD68 and (C) CD163 macroph…
View article: Data from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation
Data from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation Open
Purpose: Tumor-associated macrophages (TAMs) in malignant tumors have been linked to tumor aggressiveness and represent a new target for cancer immunotherapy. As new TAM-targeted immunotherapies are entering clinical trials, it is i…
View article: Figure S1 from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation
Figure S1 from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation Open
Figure S1 shows the method of semi-automated TAM quantification after CD68 stain on a light microscopic image. (A) CD 68+ macrophage stain (brown) of a lymphoma shows homogenous distribution. (B) and (C) Segmented areas (red) of CD68+ macr…
View article: Data from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation
Data from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation Open
Purpose: Tumor-associated macrophages (TAMs) in malignant tumors have been linked to tumor aggressiveness and represent a new target for cancer immunotherapy. As new TAM-targeted immunotherapies are entering clinical trials, it is i…
View article: Figure S1 from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation
Figure S1 from Magnetic Resonance Imaging of Tumor-Associated Macrophages: Clinical Translation Open
Figure S1 shows the method of semi-automated TAM quantification after CD68 stain on a light microscopic image. (A) CD 68+ macrophage stain (brown) of a lymphoma shows homogenous distribution. (B) and (C) Segmented areas (red) of CD68+ macr…
View article: RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models Open
Dave Van Veen, Cara Van Uden, Maayane Attias, Anuj Pareek, Christian Bluethgen, Malgorzata Polacin, Wah Chiu, Jean-Benoit Delbrouck, Juan Zambrano Chaves, Curtis Langlotz, Akshay Chaudhari, John Pauly. The 22nd Workshop on Biomedical Natur…
View article: Benchmarking saliency methods for chest X-ray interpretation
Benchmarking saliency methods for chest X-ray interpretation Open
Saliency methods, which produce heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. However, rigorous investigation of the accu…
View article: CheXED
CheXED Open
Purpose: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support systems for the diagnosis and management of pneumonia are commonly utilized in EDs to imp…
View article: Outcomes of acute respiratory distress syndrome in COVID-19 patients compared to the general population: a systematic review and meta-analysis
Outcomes of acute respiratory distress syndrome in COVID-19 patients compared to the general population: a systematic review and meta-analysis Open
Ten studies with 2,281 patients met inclusion criteria (COVID-19: 861 [37.7%], ARDS: 1420 [62.3%]). There were no significant differences between the COVID-19 and ARDS groups for median number of mechanical ventilator-free days (MDM: -7.0 …
View article: Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT
Multi-task weak supervision enables anatomically-resolved abnormality detection in whole-body FDG-PET/CT Open
Computational decision support systems could provide clinical value in whole-body FDG-PET/CT workflows. However, limited availability of labeled data combined with the large size of PET/CT imaging exams make it challenging to apply existin…
View article: CheXternal
CheXternal Open
Recent advances in training deep learning models have demonstrated the potential to provide accurate chest X-ray interpretation and increase access to radiology expertise. However, poor generalization due to data distribution shifts in cli…
View article: Benchmarking saliency methods for chest X-ray interpretation
Benchmarking saliency methods for chest X-ray interpretation Open
Saliency methods, which “explain” deep neural networks by producing heat maps that highlight the areas of the medical image that influence model prediction, are often presented to clinicians as an aid in diagnostic decision-making. Althoug…
View article: Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Counterfactual Generation for Chest X-rays Open
Motivation: Traditional image attribution methods struggle to satisfactorily explain predictions of neural networks. Prediction explanation is important, especially in medical imaging, for avoiding the unintended consequences of deploying …
View article: Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Progressive Exaggeration on Chest X-rays.
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Progressive Exaggeration on Chest X-rays. Open
Motivation: Traditional image attribution methods struggle to satisfactorily explain predictions of neural networks. Prediction explanation is important, especially in the medical imaging, for avoiding the unintended consequences of deploy…
View article: Outcomes of acute respiratory distress syndrome in COVID-19 patients compared to the general population: a systematic review and meta-analysis
Outcomes of acute respiratory distress syndrome in COVID-19 patients compared to the general population: a systematic review and meta-analysis Open
Acute respiratory distress syndrome (ARDS) due to coronavirus disease 2019 (COVID-19) often leads to mortality. Outcomes of patients with COVID-19-related ARDS compared to ARDS unrelated to COVID-19 is not well characterized. We performed …
View article: Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection
Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection Open
View article: Clinical Characteristics and Outcomes in Patients with COVID-19 and Cancer: a Systematic Review and Meta-analysis
Clinical Characteristics and Outcomes in Patients with COVID-19 and Cancer: a Systematic Review and Meta-analysis Open
View article: CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays
CheXphotogenic: Generalization of Deep Learning Models for Chest X-ray Interpretation to Photos of Chest X-rays Open
The use of smartphones to take photographs of chest x-rays represents an appealing solution for scaled deployment of deep learning models for chest x-ray interpretation. However, the performance of chest x-ray algorithms on photos of chest…
View article: Deep learning and its role in COVID-19 medical imaging
Deep learning and its role in COVID-19 medical imaging Open